Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations8693
Missing cells2324
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory891.5 KiB
Average record size in memory105.0 B

Variable types

Text3
Categorical2
Boolean3
Numeric6

Alerts

FoodCourt is highly overall correlated with VRDeckHigh correlation
VRDeck is highly overall correlated with FoodCourtHigh correlation
VIP is highly imbalanced (84.0%)Imbalance
HomePlanet has 201 (2.3%) missing valuesMissing
CryoSleep has 217 (2.5%) missing valuesMissing
Cabin has 199 (2.3%) missing valuesMissing
Destination has 182 (2.1%) missing valuesMissing
Age has 179 (2.1%) missing valuesMissing
VIP has 203 (2.3%) missing valuesMissing
RoomService has 181 (2.1%) missing valuesMissing
FoodCourt has 183 (2.1%) missing valuesMissing
ShoppingMall has 208 (2.4%) missing valuesMissing
Spa has 183 (2.1%) missing valuesMissing
VRDeck has 188 (2.2%) missing valuesMissing
Name has 200 (2.3%) missing valuesMissing
PassengerId has unique valuesUnique
Age has 178 (2.0%) zerosZeros
RoomService has 5577 (64.2%) zerosZeros
FoodCourt has 5456 (62.8%) zerosZeros
ShoppingMall has 5587 (64.3%) zerosZeros
Spa has 5324 (61.2%) zerosZeros
VRDeck has 5495 (63.2%) zerosZeros

Reproduction

Analysis started2025-10-25 06:03:19.439399
Analysis finished2025-10-25 06:03:25.542105
Duration6.1 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

PassengerId
Text

Unique 

Distinct8693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:25.903031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60851
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8693 ?
Unique (%)100.0%

Sample

1st row0001_01
2nd row0002_01
3rd row0003_01
4th row0003_02
5th row0004_01
ValueCountFrequency (%)
0005_011
 
< 0.1%
9280_021
 
< 0.1%
0001_011
 
< 0.1%
0002_011
 
< 0.1%
0003_011
 
< 0.1%
9264_011
 
< 0.1%
9267_011
 
< 0.1%
9267_021
 
< 0.1%
9268_011
 
< 0.1%
9270_011
 
< 0.1%
Other values (8683)8683
99.9%
2025-10-25T06:03:26.379747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
012459
20.5%
19827
16.1%
_8693
14.3%
25017
8.2%
34039
 
6.6%
43790
 
6.2%
63664
 
6.0%
53606
 
5.9%
83557
 
5.8%
73410
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)60851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012459
20.5%
19827
16.1%
_8693
14.3%
25017
8.2%
34039
 
6.6%
43790
 
6.2%
63664
 
6.0%
53606
 
5.9%
83557
 
5.8%
73410
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012459
20.5%
19827
16.1%
_8693
14.3%
25017
8.2%
34039
 
6.6%
43790
 
6.2%
63664
 
6.0%
53606
 
5.9%
83557
 
5.8%
73410
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012459
20.5%
19827
16.1%
_8693
14.3%
25017
8.2%
34039
 
6.6%
43790
 
6.2%
63664
 
6.0%
53606
 
5.9%
83557
 
5.8%
73410
 
5.6%

HomePlanet
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing201
Missing (%)2.3%
Memory size68.0 KiB
Earth
4602 
Europa
2131 
Mars
1759 

Length

Max length6
Median length5
Mean length5.0438059
Min length4

Characters and Unicode

Total characters42832
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth4602
52.9%
Europa2131
24.5%
Mars1759
 
20.2%
(Missing)201
 
2.3%

Length

2025-10-25T06:03:26.510062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-25T06:03:26.594873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
earth4602
54.2%
europa2131
25.1%
mars1759
 
20.7%

Most occurring characters

ValueCountFrequency (%)
a8492
19.8%
r8492
19.8%
E6733
15.7%
t4602
10.7%
h4602
10.7%
u2131
 
5.0%
o2131
 
5.0%
p2131
 
5.0%
M1759
 
4.1%
s1759
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)42832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8492
19.8%
r8492
19.8%
E6733
15.7%
t4602
10.7%
h4602
10.7%
u2131
 
5.0%
o2131
 
5.0%
p2131
 
5.0%
M1759
 
4.1%
s1759
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8492
19.8%
r8492
19.8%
E6733
15.7%
t4602
10.7%
h4602
10.7%
u2131
 
5.0%
o2131
 
5.0%
p2131
 
5.0%
M1759
 
4.1%
s1759
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8492
19.8%
r8492
19.8%
E6733
15.7%
t4602
10.7%
h4602
10.7%
u2131
 
5.0%
o2131
 
5.0%
p2131
 
5.0%
M1759
 
4.1%
s1759
 
4.1%

CryoSleep
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing217
Missing (%)2.5%
Memory size68.0 KiB
False
5439 
True
3037 
(Missing)
 
217
ValueCountFrequency (%)
False5439
62.6%
True3037
34.9%
(Missing)217
 
2.5%
2025-10-25T06:03:26.668409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Cabin
Text

Missing 

Distinct6560
Distinct (%)77.2%
Missing199
Missing (%)2.3%
Memory size68.0 KiB
2025-10-25T06:03:26.987529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0775842
Min length5

Characters and Unicode

Total characters60117
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5427 ?
Unique (%)63.9%

Sample

1st rowB/0/P
2nd rowF/0/S
3rd rowA/0/S
4th rowA/0/S
5th rowF/1/S
ValueCountFrequency (%)
g/734/s8
 
0.1%
c/137/s7
 
0.1%
g/1476/s7
 
0.1%
b/11/s7
 
0.1%
f/1194/p7
 
0.1%
b/82/s7
 
0.1%
d/176/s7
 
0.1%
g/981/s7
 
0.1%
e/13/s7
 
0.1%
f/1411/p7
 
0.1%
Other values (6550)8423
99.2%
2025-10-25T06:03:27.457675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/16988
28.3%
15326
 
8.9%
S4288
 
7.1%
P4206
 
7.0%
23078
 
5.1%
F2794
 
4.6%
32601
 
4.3%
G2559
 
4.3%
42393
 
4.0%
52377
 
4.0%
Other values (11)13507
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)60117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/16988
28.3%
15326
 
8.9%
S4288
 
7.1%
P4206
 
7.0%
23078
 
5.1%
F2794
 
4.6%
32601
 
4.3%
G2559
 
4.3%
42393
 
4.0%
52377
 
4.0%
Other values (11)13507
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/16988
28.3%
15326
 
8.9%
S4288
 
7.1%
P4206
 
7.0%
23078
 
5.1%
F2794
 
4.6%
32601
 
4.3%
G2559
 
4.3%
42393
 
4.0%
52377
 
4.0%
Other values (11)13507
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/16988
28.3%
15326
 
8.9%
S4288
 
7.1%
P4206
 
7.0%
23078
 
5.1%
F2794
 
4.6%
32601
 
4.3%
G2559
 
4.3%
42393
 
4.0%
52377
 
4.0%
Other values (11)13507
22.5%

Destination
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing182
Missing (%)2.1%
Memory size68.0 KiB
TRAPPIST-1e
5915 
55 Cancri e
1800 
PSO J318.5-22
796 

Length

Max length13
Median length11
Mean length11.187052
Min length11

Characters and Unicode

Total characters95213
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowTRAPPIST-1e
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e5915
68.0%
55 Cancri e1800
 
20.7%
PSO J318.5-22796
 
9.2%
(Missing)182
 
2.1%

Length

2025-10-25T06:03:27.584190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-25T06:03:27.659360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e5915
45.8%
551800
 
13.9%
cancri1800
 
13.9%
e1800
 
13.9%
pso796
 
6.2%
j318.5-22796
 
6.2%

Most occurring characters

ValueCountFrequency (%)
P12626
13.3%
T11830
12.4%
e7715
 
8.1%
-6711
 
7.0%
S6711
 
7.0%
16711
 
7.0%
R5915
 
6.2%
I5915
 
6.2%
A5915
 
6.2%
54396
 
4.6%
Other values (13)20768
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)95213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P12626
13.3%
T11830
12.4%
e7715
 
8.1%
-6711
 
7.0%
S6711
 
7.0%
16711
 
7.0%
R5915
 
6.2%
I5915
 
6.2%
A5915
 
6.2%
54396
 
4.6%
Other values (13)20768
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)95213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P12626
13.3%
T11830
12.4%
e7715
 
8.1%
-6711
 
7.0%
S6711
 
7.0%
16711
 
7.0%
R5915
 
6.2%
I5915
 
6.2%
A5915
 
6.2%
54396
 
4.6%
Other values (13)20768
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)95213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P12626
13.3%
T11830
12.4%
e7715
 
8.1%
-6711
 
7.0%
S6711
 
7.0%
16711
 
7.0%
R5915
 
6.2%
I5915
 
6.2%
A5915
 
6.2%
54396
 
4.6%
Other values (13)20768
21.8%

Age
Real number (ℝ)

Missing  Zeros 

Distinct80
Distinct (%)0.9%
Missing179
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean28.82793
Minimum0
Maximum79
Zeros178
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:27.787791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q119
median27
Q338
95-th percentile56
Maximum79
Range79
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.489021
Coefficient of variation (CV)0.50260359
Kurtosis0.10193292
Mean28.82793
Median Absolute Deviation (MAD)9
Skewness0.41909658
Sum245441
Variance209.93174
MonotonicityNot monotonic
2025-10-25T06:03:27.923433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24324
 
3.7%
18320
 
3.7%
21311
 
3.6%
19293
 
3.4%
23292
 
3.4%
22291
 
3.3%
20277
 
3.2%
26268
 
3.1%
28267
 
3.1%
27259
 
3.0%
Other values (70)5612
64.6%
ValueCountFrequency (%)
0178
2.0%
167
 
0.8%
275
0.9%
375
0.9%
471
 
0.8%
533
 
0.4%
640
 
0.5%
752
 
0.6%
846
 
0.5%
942
 
0.5%
ValueCountFrequency (%)
793
 
< 0.1%
783
 
< 0.1%
772
 
< 0.1%
762
 
< 0.1%
754
< 0.1%
745
0.1%
737
0.1%
724
< 0.1%
717
0.1%
709
0.1%

VIP
Boolean

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing203
Missing (%)2.3%
Memory size68.0 KiB
False
8291 
True
 
199
(Missing)
 
203
ValueCountFrequency (%)
False8291
95.4%
True199
 
2.3%
(Missing)203
 
2.3%
2025-10-25T06:03:28.013187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

RoomService
Real number (ℝ)

Missing  Zeros 

Distinct1273
Distinct (%)15.0%
Missing181
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean224.68762
Minimum0
Maximum14327
Zeros5577
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:28.107010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q347
95-th percentile1274.25
Maximum14327
Range14327
Interquartile range (IQR)47

Descriptive statistics

Standard deviation666.71766
Coefficient of variation (CV)2.9673093
Kurtosis65.273802
Mean224.68762
Median Absolute Deviation (MAD)0
Skewness6.3330141
Sum1912541
Variance444512.44
MonotonicityNot monotonic
2025-10-25T06:03:28.238613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05577
64.2%
1117
 
1.3%
279
 
0.9%
361
 
0.7%
447
 
0.5%
528
 
0.3%
925
 
0.3%
824
 
0.3%
624
 
0.3%
1421
 
0.2%
Other values (1263)2509
28.9%
(Missing)181
 
2.1%
ValueCountFrequency (%)
05577
64.2%
1117
 
1.3%
279
 
0.9%
361
 
0.7%
447
 
0.5%
528
 
0.3%
624
 
0.3%
717
 
0.2%
824
 
0.3%
925
 
0.3%
ValueCountFrequency (%)
143271
< 0.1%
99201
< 0.1%
85861
< 0.1%
82431
< 0.1%
82091
< 0.1%
81681
< 0.1%
81511
< 0.1%
81421
< 0.1%
80301
< 0.1%
74061
< 0.1%

FoodCourt
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1507
Distinct (%)17.7%
Missing183
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean458.0772
Minimum0
Maximum29813
Zeros5456
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:28.366486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q376
95-th percentile2748.5
Maximum29813
Range29813
Interquartile range (IQR)76

Descriptive statistics

Standard deviation1611.4892
Coefficient of variation (CV)3.5179425
Kurtosis73.30723
Mean458.0772
Median Absolute Deviation (MAD)0
Skewness7.1022279
Sum3898237
Variance2596897.6
MonotonicityNot monotonic
2025-10-25T06:03:28.498899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05456
62.8%
1116
 
1.3%
275
 
0.9%
353
 
0.6%
453
 
0.6%
533
 
0.4%
631
 
0.4%
928
 
0.3%
1027
 
0.3%
727
 
0.3%
Other values (1497)2611
30.0%
(Missing)183
 
2.1%
ValueCountFrequency (%)
05456
62.8%
1116
 
1.3%
275
 
0.9%
353
 
0.6%
453
 
0.6%
533
 
0.4%
631
 
0.4%
727
 
0.3%
820
 
0.2%
928
 
0.3%
ValueCountFrequency (%)
298131
< 0.1%
277231
< 0.1%
270711
< 0.1%
268301
< 0.1%
210661
< 0.1%
184811
< 0.1%
179581
< 0.1%
179011
< 0.1%
176871
< 0.1%
174321
< 0.1%

ShoppingMall
Real number (ℝ)

Missing  Zeros 

Distinct1115
Distinct (%)13.1%
Missing208
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean173.72917
Minimum0
Maximum23492
Zeros5587
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:28.625977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327
95-th percentile927.8
Maximum23492
Range23492
Interquartile range (IQR)27

Descriptive statistics

Standard deviation604.69646
Coefficient of variation (CV)3.4806847
Kurtosis328.87091
Mean173.72917
Median Absolute Deviation (MAD)0
Skewness12.627562
Sum1474092
Variance365657.81
MonotonicityNot monotonic
2025-10-25T06:03:28.780733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05587
64.3%
1153
 
1.8%
280
 
0.9%
359
 
0.7%
445
 
0.5%
538
 
0.4%
736
 
0.4%
634
 
0.4%
1329
 
0.3%
928
 
0.3%
Other values (1105)2396
27.6%
(Missing)208
 
2.4%
ValueCountFrequency (%)
05587
64.3%
1153
 
1.8%
280
 
0.9%
359
 
0.7%
445
 
0.5%
538
 
0.4%
634
 
0.4%
736
 
0.4%
828
 
0.3%
928
 
0.3%
ValueCountFrequency (%)
234921
< 0.1%
122531
< 0.1%
107051
< 0.1%
104241
< 0.1%
90581
< 0.1%
78101
< 0.1%
71851
< 0.1%
71481
< 0.1%
71041
< 0.1%
68051
< 0.1%

Spa
Real number (ℝ)

Missing  Zeros 

Distinct1327
Distinct (%)15.6%
Missing183
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean311.13878
Minimum0
Maximum22408
Zeros5324
Zeros (%)61.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:28.904326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q359
95-th percentile1607.1
Maximum22408
Range22408
Interquartile range (IQR)59

Descriptive statistics

Standard deviation1136.7055
Coefficient of variation (CV)3.6533715
Kurtosis81.20211
Mean311.13878
Median Absolute Deviation (MAD)0
Skewness7.6360199
Sum2647791
Variance1292099.5
MonotonicityNot monotonic
2025-10-25T06:03:29.263133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05324
61.2%
1146
 
1.7%
2105
 
1.2%
353
 
0.6%
553
 
0.6%
446
 
0.5%
734
 
0.4%
633
 
0.4%
928
 
0.3%
828
 
0.3%
Other values (1317)2660
30.6%
(Missing)183
 
2.1%
ValueCountFrequency (%)
05324
61.2%
1146
 
1.7%
2105
 
1.2%
353
 
0.6%
446
 
0.5%
553
 
0.6%
633
 
0.4%
734
 
0.4%
828
 
0.3%
928
 
0.3%
ValueCountFrequency (%)
224081
< 0.1%
185721
< 0.1%
165941
< 0.1%
161391
< 0.1%
155861
< 0.1%
153311
< 0.1%
152381
< 0.1%
149701
< 0.1%
139951
< 0.1%
139021
< 0.1%

VRDeck
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1306
Distinct (%)15.4%
Missing188
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean304.85479
Minimum0
Maximum24133
Zeros5495
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2025-10-25T06:03:29.389229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q346
95-th percentile1534.2
Maximum24133
Range24133
Interquartile range (IQR)46

Descriptive statistics

Standard deviation1145.7172
Coefficient of variation (CV)3.7582391
Kurtosis86.011186
Mean304.85479
Median Absolute Deviation (MAD)0
Skewness7.8197316
Sum2592790
Variance1312667.9
MonotonicityNot monotonic
2025-10-25T06:03:29.544082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05495
63.2%
1139
 
1.6%
270
 
0.8%
356
 
0.6%
551
 
0.6%
447
 
0.5%
632
 
0.4%
830
 
0.3%
729
 
0.3%
925
 
0.3%
Other values (1296)2531
29.1%
(Missing)188
 
2.2%
ValueCountFrequency (%)
05495
63.2%
1139
 
1.6%
270
 
0.8%
356
 
0.6%
447
 
0.5%
551
 
0.6%
632
 
0.4%
729
 
0.3%
830
 
0.3%
925
 
0.3%
ValueCountFrequency (%)
241331
< 0.1%
203361
< 0.1%
173061
< 0.1%
170741
< 0.1%
163371
< 0.1%
144851
< 0.1%
127081
< 0.1%
126851
< 0.1%
126821
< 0.1%
124241
< 0.1%

Name
Text

Missing 

Distinct8473
Distinct (%)99.8%
Missing200
Missing (%)2.3%
Memory size68.0 KiB
2025-10-25T06:03:29.982674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.833628
Min length7

Characters and Unicode

Total characters117489
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8453 ?
Unique (%)99.5%

Sample

1st rowMaham Ofracculy
2nd rowJuanna Vines
3rd rowAltark Susent
4th rowSolam Susent
5th rowWilly Santantines
ValueCountFrequency (%)
willy20
 
0.1%
casonston18
 
0.1%
oneiles16
 
0.1%
domington15
 
0.1%
litthews15
 
0.1%
cartez14
 
0.1%
garnes14
 
0.1%
fulloydez14
 
0.1%
browlerson14
 
0.1%
distured13
 
0.1%
Other values (4880)16833
99.1%
2025-10-25T06:03:30.597055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e12691
 
10.8%
a10251
 
8.7%
n9155
 
7.8%
8493
 
7.2%
r7707
 
6.6%
o6563
 
5.6%
i6456
 
5.5%
l6231
 
5.3%
s5299
 
4.5%
t4552
 
3.9%
Other values (43)40091
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)117489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e12691
 
10.8%
a10251
 
8.7%
n9155
 
7.8%
8493
 
7.2%
r7707
 
6.6%
o6563
 
5.6%
i6456
 
5.5%
l6231
 
5.3%
s5299
 
4.5%
t4552
 
3.9%
Other values (43)40091
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e12691
 
10.8%
a10251
 
8.7%
n9155
 
7.8%
8493
 
7.2%
r7707
 
6.6%
o6563
 
5.6%
i6456
 
5.5%
l6231
 
5.3%
s5299
 
4.5%
t4552
 
3.9%
Other values (43)40091
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e12691
 
10.8%
a10251
 
8.7%
n9155
 
7.8%
8493
 
7.2%
r7707
 
6.6%
o6563
 
5.6%
i6456
 
5.5%
l6231
 
5.3%
s5299
 
4.5%
t4552
 
3.9%
Other values (43)40091
34.1%

Transported
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
True
4378 
False
4315 
ValueCountFrequency (%)
True4378
50.4%
False4315
49.6%
2025-10-25T06:03:30.721211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-10-25T06:03:24.079572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:20.604623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.292963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.144724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.806648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.433698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:24.193009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:20.734113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.424643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.259320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.917373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.564157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:24.306287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:20.849694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.692195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.371038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.024564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.673435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:24.415408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:20.963050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.805160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.497737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.136546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.781489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:24.711477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.072126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.916562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.599350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.231342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.875184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:24.825823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:21.183375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.024440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:22.702861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.328337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-25T06:03:23.967904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-25T06:03:30.812290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCryoSleepDestinationFoodCourtHomePlanetRoomServiceShoppingMallSpaTransportedVIPVRDeck
Age1.0000.1120.0410.2080.2010.1230.1030.1970.1340.1180.181
CryoSleep0.1121.0000.1190.1610.1180.1530.0700.1400.4680.0800.127
Destination0.0410.1191.0000.0920.2620.0350.0090.0680.1110.0430.062
FoodCourt0.2080.1610.0921.0000.2620.1850.1870.4860.0600.1330.511
HomePlanet0.2010.1180.2620.2621.0000.1500.0540.1900.1950.1770.197
RoomService0.1230.1530.0350.1850.1501.0000.4430.2490.1620.0540.182
ShoppingMall0.1030.0700.0090.1870.0540.4431.0000.2570.0390.0000.194
Spa0.1970.1400.0680.4860.1900.2490.2571.0000.1750.0440.448
Transported0.1340.4680.1110.0600.1950.1620.0390.1751.0000.0350.155
VIP0.1180.0800.0430.1330.1770.0540.0000.0440.0351.0000.120
VRDeck0.1810.1270.0620.5110.1970.1820.1940.4480.1550.1201.000

Missing values

2025-10-25T06:03:24.983971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-25T06:03:25.147669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-25T06:03:25.411226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameTransported
00001_01EuropaFalseB/0/PTRAPPIST-1e39.0False0.00.00.00.00.0Maham OfracculyFalse
10002_01EarthFalseF/0/STRAPPIST-1e24.0False109.09.025.0549.044.0Juanna VinesTrue
20003_01EuropaFalseA/0/STRAPPIST-1e58.0True43.03576.00.06715.049.0Altark SusentFalse
30003_02EuropaFalseA/0/STRAPPIST-1e33.0False0.01283.0371.03329.0193.0Solam SusentFalse
40004_01EarthFalseF/1/STRAPPIST-1e16.0False303.070.0151.0565.02.0Willy SantantinesTrue
50005_01EarthFalseF/0/PPSO J318.5-2244.0False0.0483.00.0291.00.0Sandie HinetthewsTrue
60006_01EarthFalseF/2/STRAPPIST-1e26.0False42.01539.03.00.00.0Billex JacostaffeyTrue
70006_02EarthTrueG/0/STRAPPIST-1e28.0False0.00.00.00.0NaNCandra JacostaffeyTrue
80007_01EarthFalseF/3/STRAPPIST-1e35.0False0.0785.017.0216.00.0Andona BestonTrue
90008_01EuropaTrueB/1/P55 Cancri e14.0False0.00.00.00.00.0Erraiam FlaticTrue
PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameTransported
86839272_02EarthFalseF/1894/PTRAPPIST-1e21.0False86.03.0149.0208.0329.0Gordo SimsonFalse
86849274_01NaNTrueG/1508/PTRAPPIST-1e23.0False0.00.00.00.00.0Chelsa BulliseyTrue
86859275_01EuropaFalseA/97/PTRAPPIST-1e0.0False0.00.00.00.00.0Polaton ConableTrue
86869275_02EuropaFalseA/97/PTRAPPIST-1e32.0False1.01146.00.050.034.0Diram ConableFalse
86879275_03EuropaNaNA/97/PTRAPPIST-1e30.0False0.03208.00.02.0330.0Atlasym ConableTrue
86889276_01EuropaFalseA/98/P55 Cancri e41.0True0.06819.00.01643.074.0Gravior NoxnutherFalse
86899278_01EarthTrueG/1499/SPSO J318.5-2218.0False0.00.00.00.00.0Kurta MondalleyFalse
86909279_01EarthFalseG/1500/STRAPPIST-1e26.0False0.00.01872.01.00.0Fayey ConnonTrue
86919280_01EuropaFalseE/608/S55 Cancri e32.0False0.01049.00.0353.03235.0Celeon HontichreFalse
86929280_02EuropaFalseE/608/STRAPPIST-1e44.0False126.04688.00.00.012.0Propsh HontichreTrue